distilbert-base-uncased-WNUT-ner – AI Model by dmargutierrez | AlphaNeural AI

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dmargutierrez/
distilbert-base-uncased-WNUT-ner

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token-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-WNUT-ner

This model is a fine-tuned version of distilbert-base-uncased on the wnut_17 dataset. It achieves the following results on the evaluation set:

Loss: 0.3510
Precision: 0.5362
Recall: 0.3911
F1: 0.4523
Accuracy: 0.9465

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	213	0.2745	0.5099	0.2614	0.3456	0.9392
No log	2.0	426	0.2729	0.6080	0.3105	0.4110	0.9436
0.1878	3.0	639	0.2826	0.5769	0.3373	0.4257	0.9456
0.1878	4.0	852	0.2840	0.5440	0.3726	0.4422	0.9461
0.0465	5.0	1065	0.3286	0.5914	0.3568	0.4451	0.9461
0.0465	6.0	1278	0.3273	0.5693	0.3577	0.4394	0.9466
0.0465	7.0	1491	0.3462	0.5621	0.3818	0.4547	0.9469
0.0195	8.0	1704	0.3576	0.5442	0.3707	0.4410	0.9461
0.0195	9.0	1917	0.3563	0.5306	0.3698	0.4358	0.9460
0.0111	10.0	2130	0.3510	0.5362	0.3911	0.4523	0.9465

Framework versions

Transformers 4.26.0
Pytorch 1.13.1+cu117
Datasets 2.9.0
Tokenizers 0.13.2